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  • model adequacy and the macroevolution of angiosperm functional traits
    The American Naturalist, 2015
    Co-Authors: Matthew W Pennell, Richard G Fitzjohn, William K Cornwell, Luke J. Harmon
    Abstract:

    AbstractMaking meaningful inferences from phylogenetic Comparative Data requires a meaningful model of trait evolution. It is thus important to determine whether the model is appropriate for the Data and the question being addressed. One way to assess this is to ask whether the model provides a good statistical explanation for the variation in the Data. To date, researchers have focused primarily on the explanatory power of a model relative to alternative models. Methods have been developed to assess the adequacy, or absolute explanatory power, of phylogenetic trait models, but these have been restricted to specific models or questions. Here we present a general statistical framework for assessing the adequacy of phylogenetic trait models. We use our approach to evaluate the statistical performance of commonly used trait models on 337 Comparative Data sets covering three key angiosperm functional traits. In general, the models we tested often provided poor statistical explanations for the evolution of the...

  • a novel bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic Comparative Data
    Systematic Biology, 2014
    Co-Authors: Josef C Uyeda, Luke J. Harmon
    Abstract:

    Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic Comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from Comparative Data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here, we develop a reversible-jump Bayesian method of fitting multi- optima OU models to phylogenetic Comparative Data that estimates the placement and magnitude of adaptive shifts directly from the Data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model fitting of OU models to Comparative Data provides a framework for integrating of multiple sources of biological Data—such as microevolutionary estimates of selection parameters and paleontological timeseries—allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations. (bayou; Comparative methods; macroevolution; Ornstein-Uhlenbeck; Reversible-jump models.)

  • a novel bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic Comparative Data
    bioRxiv, 2014
    Co-Authors: Josef C Uyeda, Luke J. Harmon
    Abstract:

    Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic Comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from Comparative Data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic Comparative Data that estimates the placement and magnitude of adaptive shifts directly from the Data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model-fitting of OU models to Comparative Data provides a framework for integrating of multiple sources of biological Data--such as microevolutionary estimates of selection parameters and paleontological timeseries--allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.

  • fitting models of continuous trait evolution to incompletely sampled Comparative Data using approximate bayesian computation
    Evolution, 2012
    Co-Authors: Graham J Slater, Luke J. Harmon, Daniel Wegmann, Paul Joyce, Liam J Revell, Michael E Alfaro
    Abstract:

    In recent years, a suite of methods has been developed tofit multiple rate models to phylogenetic Comparative Data. However, most methods have limited utility at broad phylogenetic scales because they typically require complete sampling of both the tree and the associated phenotypic Data. Here, we develop and implement a new, tree-based method called MECCA (Modeling Evolution of Continuous Characters using ABC) that uses a hybrid likelihood/approximate Bayesian computation (ABC)-Markov-Chain Monte Carlo approach to simultaneously infer rates of diversification and trait evolution from incompletely sampled phylogenies and trait Data. We demonstrate via simulation that MECCA has considerable power to choose among single versus multiple evolutionary rate models, and thus can be used to test hypotheses about changes in the rate of trait evolution across an incomplete tree of life. We finally apply MECCA to an empirical example of body size evolution in carnivores, and show that there is no evidence for an elevated rate of body size evolution in the pinnipeds relative to terrestrial carnivores. ABC approaches can provide a useful alternative set of tools for future macroevolutionary studies where likelihood-dependent approaches are lacking.

  • early bursts of body size and shape evolution are rare in Comparative Data
    Evolution, 2010
    Co-Authors: Luke J. Harmon, Jonathan B Losos, Jonathan T Davies, Rosemary G Gillespie, John L Gittleman, Bryan W Jennings, Kenneth H Kozak, Mark A Mcpeek, Franck Morenoroark
    Abstract:

    George Gaylord Simpson famously postulated that much of life's diversity originated as adaptive radiations-more or less simultaneous divergences of numerous lines from a single ancestral adaptive type. However, identifying adaptive radiations has proven difficult due to a lack of broad-scale Comparative Datasets. Here, we use phylogenetic Comparative Data on body size and shape in a diversity of animal clades to test a key model of adaptive radiation, in which initially rapid morphological evolution is followed by relative stasis. We compared the fit of this model to both single selective peak and random walk models. We found little support for the early-burst model of adaptive radiation, whereas both other models, particularly that of selective peaks, were commonly supported. In addition, we found that the net rate of morphological evolution varied inversely with clade age. The youngest clades appear to evolve most rapidly because long-term change typically does not attain the amount of divergence predicted from rates measured over short time scales. Across our entire analysis, the dominant pattern was one of constraints shaping evolution continually through time rather than rapid evolution followed by stasis. We suggest that the classical model of adaptive radiation, where morphological evolution is initially rapid and slows through time, may be rare in Comparative Data.

Shaan Dudani - One of the best experts on this subject based on the ideXlab platform.

Aaron R Hansen - One of the best experts on this subject based on the ideXlab platform.

Ziad Bakouny - One of the best experts on this subject based on the ideXlab platform.